期刊
ENERGY CONVERSION AND MANAGEMENT
卷 105, 期 -, 页码 642-654出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2015.08.025
关键词
Price forecast; Modified chemical reaction optimization; Feature selection
资金
- FEDER funds (European Union) through COMPETE
- FCT [FCOMP-01-0124-FEDER-020282, PTDC/EEA-EEL/118519/2010, UID/CEC/50021/2013]
- EU [309048]
- Fundação para a Ciência e a Tecnologia [PTDC/EEA-EEL/118519/2010] Funding Source: FCT
Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania-New Jersey-Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy. (C) 2015 Elsevier Ltd. All rights reserved.
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